Bridging Probabilistic Inference and Behavior Trees: An Interactive Framework for Adaptive Multi-Robot Cooperation

TL;DR

This paper introduces an Interactive Inference Behavior Tree (IIBT) framework that combines probabilistic reasoning with behavior trees for adaptive, distributed multi-robot cooperation, improving decision-making in uncertain environments.

Contribution

It presents a novel IIBT node that integrates active inference into behavior trees, enabling online joint planning and seamless integration with existing systems.

Findings

Reduces BT node complexity by over 70%.

Achieves adaptive coordination in dynamic environments.

Demonstrates robustness in real-world multi-robot tasks.

Abstract

This paper proposes an Interactive Inference Behavior Tree (IIBT) framework that integrates behavior trees (BTs) with active inference under the free energy principle for distributed multi-robot decision-making. The proposed IIBT node extends conventional BTs with probabilistic reasoning, enabling online joint planning and execution across multiple robots. It remains fully compatible with standard BT architectures, allowing seamless integration into existing multi-robot control systems. Within this framework, multi-robot cooperation is formulated as a free-energy minimization process, where each robot dynamically updates its preference matrix based on perceptual inputs and peer intentions, thereby achieving adaptive coordination in partially observable and dynamic environments. The proposed approach is validated through both simulation and real-world experiments, including a multi-robot maze navigation and a collaborative manipulation task, compared against traditional BTs(https://youtu.be/KX_oT3IDTf4). Experimental results demonstrate that the IIBT framework reduces BT node complexity by over 70%, while maintaining robust, interpretable, and adaptive cooperative behavior under environmental uncertainty.